14.1. Map calculations

In a grid, each pixel has a characteristic attached to it.
This can be a code but also a number, as for example water level or topographic
level. Once grids are compared in the same location, it becomes straightforward
that you can make calculations between grids.

Let us look at an example. In Figure 14.1 a cross-section is
presented from the floodplains in Pais Pesca with the brown line representing
the topography of the land and the blue line the water level.

FIGURE 14.1Cross-section of the floodplain of Pais
Pesca

In the cross-section, the blue line is a surface plot
generated for water levels (like the one you made before) and you see
immediately that there is water in places where the topo-level (brown line) is
lower than the surface plot. To make a flood map, i.e. the real extent of
flooding, you need the generated water level grid and a grid representing the
topography of the floodplains.

Both grid files have values attached to each pixel. This
allows us to calculate the water depth at each location presented by a pixel by
simply subtracting the value of the topo-level from the water level.

Water depth = Water level-Topo level

An example of this calculation is presented in Table 14.1, and
it is clear that once the value of the water depth becomes negative the area is
dry.

TABLE 14.1Example of calculation of water depth in the
floodplain of Pais Pesca

Water Level(cm above sealevel)

Topo level(cm above sealevel)

Water level-Topo level(cm)

1 280

1 300

-20

Dry

1 310

1 280

30

Flooded

1 310

1 210

100

Flooded

1 310

1 100

210

Flooded

1 360

1 100

260

Flooded

1 380

1 223

157

Flooded

1 300

1 250

50

Flooded

1 290

1 300

-10

Dry

1 290

1 300

-10

Dry

1 280

1 350

-70

Dry

The same procedure, but now in ArcView:

1. Start ArcView, open a New Project, and a New
View. Add the Themes Pais_Pesca_country.shp,
Flood_districts.shp, Flood_district_topo.shp, and
Flood_levels.shp from the 12_Calc_with_grids folder.
Project the view (via the menu bar:
View/Properties.../Projection..., Equal area
cylindrical), and set the distance units to meters.

2. Make first a topographic map of the floodplain districts
involved: First you want to make a grid of the Flood districts.shp
Theme (Theme/Convert to Grid..., Grid name:
Mask, Output Grid extend Same as View, Cellsize 100 meters), set
this grid as a mask in the analysis properties, and only after that interpolate
the topolevels.

3. Make the Flood_district_topo.shp Theme active,
go via the menu bar to Surface/Interpolate Grid... (output
grid extend: Same as View, Cell size 100 meters, Method: IDW, Z-value field:
what do you think?[16]). This interpolation may
take a long time. Save the grid as Floodtopo in a folder you choose.
After the interpolation, you can load the legend that was made for this grid
(Topo.avl). If you do not remember how to load a legend, please have
a look at the tip on page 53.

FIGURE 14.2The topographic level of the districts
involved

If the interpolation takes too long, you can stop the process
and load the interpolated grid. (From the folder 12_Calc_with_grids,
Data Source Type: Grid Data Source, floodtopo. If you can not add
the grid to your View, and you are sure the grid is in a certain subfolder,
check if the path to this subfolder follows the naming convention of ArcView
(see the remarks on the naming convention on page 52, it might be that somewhere
in the path to your file there is a space, or a name that is longer than 13
characters. After you added the grid, load the legend [in the legend editor]:
topo.avl). The results of your efforts should look like Figure
14.2.

The previous interpolation took such a long time, because
ArcView had to calculate the waterlevel for every cell in the view (3 767 cells
by 3 983 cells, resulting in a total of 15 003 961 cells!). The calculation time
can be reduced, when you reduce the number of cells that need to be calculated.
You can do this by increasing the cell size. If you increase the cell size from
100 to 1 000, the number of cells decreases to (377 * 398) = 150 046 cells. By
doing so, the grid you create will have quite a low resolution, with which it is
difficult to do meaningful analysis. Another way to reduce the number of cells
to be calculated, and keep the same resolution (CellSize: 100), is to zoom in on
the area where you want the calculation to take place, and make the Output Grid
Extent Same As Display. If you do this, you have to pay special
attention that you include the whole area you want the calculation with,
otherwise your calculation is worthless, and you have to do it again.

Now you need to make a grid with the water levels during
October. Have a look at the table of attributes of the Theme
Flood_levels.shp. You will see that there are two water levels per
record, October and June water level, for now you will make the grid with the
October water level.

4. Make sure you still have the mask set (in the
menu bar: Analysis/Properties...) (this should be
Mask, the grid you made previously at 3. of
flood_district_topo.shp). Activate the Flood_levels.shp
Theme. Go via the menu bar to Surface/Interpolate Grid...
(Output Grid Extend: Same as View [if the previous interpolation took a long
time you can reduce that this time by zooming in on Flood_levels.shp
and making the Output Grid Extend: Same As Display], Cell size 100 meters,
Method: IDW, Z-value field: what do you
think?[17]). Also this might take a long time,
and also for this interpolation the result is already in the folder if you can
not wait for the interpolation to take place (Watersurf, legend:
Water_levels.avl).

If you have interpolated the grid yourself, you can save this
grid as Watersurf (menu bar: Theme/Save Data
Set...) in a folder you choose, and change the name of this Theme into
Watersurf (Theme/Properties...). Only if you change the name of the Theme (via
the menu bar: Theme/Properties..., Theme Name:) will the
this name appear in the Table of content (on the left side of the
View).

Why do you not see a water level in the river and big lake in
the centre of the flood
districts?[18]

Now you have the two grids Watersurf and
Floodtopo of the same area in your View, both with the same grid
dimensions of 100 meters. Calculate the water depth in the floodplain with the
formula: Water depth = Water level - Topo level.

2. In the window you see the names of the grids; [Watersurf],
[Floodtopo], [Mask] and [Mask.count]. The calculation you need to carry out will
be with the first two. Simply enter the formula by double-clicking [Watersurf],
then -, followed by double-clicking [Floodtopo]. Click on
Evaluate to start the calculation, and close the Map Calculation
window after the results appear as the Theme Map calculation 1.
Save this Theme[19] as Fldepth,
before you change anything else!!! (Theme/Save Data
Set...).

3. Redo the legend of Fldepth in such a way that
water becomes various gradients of blue and dry land becomes one grade of green
(Figure 14.5). You will see that the latter is not easy, as you have to think in
negative values. It is easier to change the calculation to Water depth =
Floodtopo - Watersurf. Do the calculation again, but now first [Floodtopo].
Positive values signify dry land, while negative values become water depth
(Figure 14.6). Save this Theme as fldepth2.

FIGURE 14.5Floodmap of Pais Pesca

FIGURE 14.6Floodmap of Pais Pesca, reversed calculation

4. You can save the project as: Pais Pesca
floodmap.apr in a folder you choose.

Note:

During calculation or grid manipulation you can get the
following message:

This means that you have deleted grid files from the working
directory. NEVER DO THIS! Always use Manage Data Sources in the File menu (via
File/Manage Data Sources... in the menu bar, Figure 14.7).
If you select this, the Source Manager window will popup (Figure 14.8)
and here you can copy, move or delete grids.

FIGURE 14.7Starting the Data Source
Manager

FIGURE 14.8The Source Manager

14.2. Reclassifying

The calculation of the water depth gives a large number of
different water depths in the floodplain of Pais Pesca. However, in some
calculations we are not interested in the water depth but only whether a certain
area is flooded or not. Working with all the different water levels makes
further calculations unnecessarily cumbersome. An easy way to overcome this
problem is to reclassify the data, i.e. you give a specific value to all pixels
representing a dry (0) area and another specific value (1) to all pixels that
are flooded.

1. Continue where you stopped with the previous
exercise, or open the project you saved: Pais Pesca
Floodmap.apr.

2. Activate the Theme Fldepth.

3. Go to Analysis/Reclassify... via the
menu bar (Figure 14.9); the Reclassify Values window will pop up. For the
classification into dry and flooded we obviously only need two classes (dry and
flooded).

5. The Reclassify Values window appears again, now with
two classes. All negative values that we calculated are dry areas, so you use
the values -200 to 0 for dry land and the values 0 to 200 for flooded land.
Click OK and the new Map will appear (Figure 14.11).

FIGURE 14.9Opening the reclassify window

FIGURE 14.10Selecting two classes to reclassify

6. Give the file a new name, arrange the legend (by double
clicking the theme, or by going to Theme/Edit Legend...via
the menu bar). If you have forgotten how to arrange a Legend, please have a look
at Graphical displays in the Map View, on page 15.

7. Make yourself a flood map of June (Figure 14.12), and save
the project again.

FIGURE 14.11The new floodmap of Pais Pesca of
October

FIGURE 14.12Floodmap of June

14.3. Querying

In a query, you try to find locations or areas on a map that
fulfils certain criteria. This can be done with two criteria using two GIS
Themes but it can also be a more complicated query using a large number of
Themes.

Some examples:

Find all the areas
with a salinity level of 15 ppt and shrimp farming as a major crop. For this two
GIS themes are needed, i.e. Salinity and shrimp farms.

Find all the areas with
alluvial soils, medium rainfall and rice as major crop. Here three themes are
needed, i.e. soil map, rainfall map and land use map.

Find all the villages with
more than 50 percent fishers, more then 50 percent of Hindu house holds, average
income of these households less then 150 $/year and fishing in the river. Four
GIS themes are needed for this query, i.e. Occupation, Income, Religion, and
Catching area.

14.3.1. Shrimp farming in the coastal
areas of Pais Pesca

In the coastal provinces of Pais Pesca shrimp farming expanded
rapidly from 8 000 ha in 1992 to 86 000 ha in 2001. The applied
system[20] is extensive with low stocking
densities of post larvae, mostly obtained from the wild. The system is an
alternation of rice with shrimps, with rainfed rice during the wet season and
one crop of shrimps during the dry season. Average yields of shrimps are in the
order of 200-400 kg/ha/crop. However the expansion of shrimp farming was
unplanned and three major problems developed over time:

In the mid 1990s
the first large problems with Monodon baculo virus (MBV) arrived followed by a
serious outbreak of White Spot disease in 1998, which almost completely wiped
out the shrimp production.

Further due to the expansion
and intensification of shrimp farming a serious conflict developed between the
large shrimp farmers and the paddy farmers. This occurred as the shrimp farmers
tried to grow two crops and consequently shrimp farming extended into the wet
season resulting in a salt intrusion seriously hampering the cultivation of rice
in the same area.

Shrimp farming encroached into
the mangrove forest, the major biosphere reserve of Pais Pesca. Shrimp ponds
were constructed in acid sulphate soils in the mangrove area resulting in
serious acidification of the surface water at the beginning of the rainy
season.

In 2000 the Department of Fisheries carried out an extensive
survey in the coastal area and collected the following information:

Location and size
of the shrimp farms;

Average yields;

Water quality;

Disease occurrence.

You will make an analysis in GIS to demonstrate how grids and
querying of different grids can be used to support management options for shrimp
farming in Pais Pesca.

Shrimp farming analysis

The Ministry of Agriculture recommended reducing the number of
shrimp farms in the agriculture zones of the coastal belt, i.e. the areas with
low surface water salinities (5 ppt) in the dry season where paddy can be
transplanted early in the rainy season. Therefore it requested the Department of
Fisheries to give an indication of the consequences of this strategy.

1. Open ArcView, New Project, New View.

2. Set the working directory to a directory of your choice
(for instance: C:\FAO_GIS\Temp), the projection of the View to Equal-Area
Cylindrical, and the Distance and Map Units to meters.

3. Add the following Themes from the
13_Querying_shrimp folder to the View: Pais Pesca
country.shp, Shrimpfarms.shp, shrimp yields.shp
(all being Feature data sources), and the Grid data source Salgrid.
The folder contains also a legend for the salinity grid (Salgrid)
for use if you want to.

Salgrid is the same grid as you have made in the exercise on
page 58. Shrimpfarms.shp is a polygon file of all the shrimp farms
in the coastal area of Pais Pesca. Shrimp yield.shp a point file of
the location of each farm, its average yields, and other data. Pais pesca
country.shp is a polygon file containing the outline of the
country.

4. First you want to know how many shrimp ponds
are located in the low salinity area. You can do this with a query between two
grids. So first you have to convert the Shrimpfarms.shp to a grid.
You want to work in hectares so you choose a grid output CellSize of 100 m
(resulting in an area of 100 m by 100 m = 1 hectare). Use the Id
field for cell values, and do not forget to rename the grid to
shrimps. To prevent this calculation to take too much time you might
want to zoom in on the shrimp farms, and do the interpolation with the Output
Grid Extent: Same As Display.

You need to query this shrimp grid with the salinity grid and
use as selection criteria: shrimp ponds = true, ([Shrimps] = 1.AsGrid) and
salinity <= 5 ([Salgrid] <= 5). In other words: of all the spots in
the shrimp Theme where there is a farm present (or equal to the desired value of
the pixels, in this case 1, as there is no other value), find the spots where
the salinity (on the salgrid Theme) is equal or lower than 5 ppt.

5. Go to Analysis/Map Query...
via the menu bar (Figure 14.13).

FIGURE 14.13Opening Map Query

6. The Map Query window will pop up and you need to put
the selection criteria in the window: ([Shrimps] = 1.AsGrid) and ([Salgrid]
<= 5). You can do this in several ways: You can type this sentence in the
lower window, or you can select the arguments from the top part of the Map Query
window; First you double click on [Shrimps], then you click on =,
and then you click on 1. When you have clicked on 1 you
see in the query line appear: 1.AsGrid. This is normal, but you can
also use a number you put in yourself (like 1) which will also work.
Then click on and in the middle of the Map Query window, after which
you double click on [Salgrid], <=, and type 5. You
can not select 5 from the values part, so you have to type this
value. (Figure 14.14). Click on Evaluate.

FIGURE 14.14Selecting the criteria for the query

FIGURE 14.15Results of Querying shrimp farms and salinity

7. The query will run and after some time the results of the
query, which is called Map Query 1 will appear in the View
(Figure 14.15). First close the Map Query 1 window, and then do not forget
to save this query as SHRSAL5[21]
(Theme/Save Dataset...). If you open the Theme attribute
table of the grid you will see that there are approximately 14 800 pixels of
shrimp pond, meaning that 14 800 ha of shrimp ponds are located in the low
salinity zone. We converted the original shapefile to a grid with a cell size of
100 metres by 100 metres, which is 1 hectare. If the number differs greatly from
14 800 you need to check the projection of the view, to make sure it is Equal
Area Cylindrical.

8. To get a more clear picture you also want to know how many
farms are located in the other zones and we make the query Shrimp farms yes
and 5> salinity<=10.In other words, find the shrimp farms which are
located in areas where the salinity is higher than 5 ppt and lower or equal to
10 ppt (Figure 14.16).

FIGURE 14.16Querying with two grids and three criteria

9. Fill in Table 14.2:

TABLE 14.2Salinity and number of shrimp
farms

Salinity zone(ppt)

Shrimp farms(ha)

0-5

14 800

5-10

10-15

15-20

You see that more then 50 percent of the farms are located in
the salinity zone of 5-10 ppt. But still approximately 15 000 ha are located in
the low salinity zone and the question is if you can get more information on the
status of the farming system in this area before you decide to close them. For
this you have to look at the yields of the farms. The location and the yields of
the individual farms are available in the Shrimp yields.shp Theme.
This is a point shapefile so you can use it to generate a grid for the
yields.

10. Make a surface grid of the yields (through
interpolation, menu bar: Surface/Interpolate Grid...),
Output grid size 100 metres, with the Shrimps grid set as mask. Remember that
this might take a while! (To reduce the time, zoom in on the Shrimp
yield.shp Theme, and make the Output Grid Extent: Same As Display). After
the interpolation is completed you can load a legend for this grid named
shrimp_yield_grid.avl (in the 13_Querying_shrimp folder).

If you do it correctly you will get a grid like the one
presented in Figure 14.17.

FIGURE 14.17Shrimp farming yield in the different
salinity zones of Pais Pesca

The results change the picture somewhat. We see yields (blue)
of about 150-250 kg/ha/crop in the northeast in the salinity zone of 0-3 ppt. In
the zones of 3-12 ppt yields are in the order of 250-450 kg/ha/crop (light blue
to red) and in the south you suddenly observe very low yields (dark blue) of
100-150 kg/ha/crop in salinities from 12-15 ppt.

A first conclusion could be to recommend to the Ministry of
Agriculture to close the shrimp farms in the salinity zones of 0-3 ppt. Query
again how many hectares of shrimp farms would be closed under this
option[22]. Secondly you found another problem
in the 12-15 ppt zone. The salinity levels are favourable for culture of P.
monodon, so there must be another factor causing these very low yields in
the southern part.

In the Theme table of the Shrimp farms shapefile
we have data on Shrimp diseases and water quality. With simple queries of this
theme table you can select the farms with a high disease occurrence,
a low disease occurrence, bad water quality,
reasonable water quality, etc. Try this out (Remember: First you
have to convert the Theme shrimpfarms.shp into a grid (menu bar:
Theme/Convert to Grid...), with a cell size of 100 metres,
while picking the relevant Conversion Field, before you can query it in the Map
Query).

You will find that the low production in the south is related
to high disease occurrence and bad water quality. The
question now is what is the cause of this problem.

Add the Theme Mangrove.shp. You can see
that the farms with the low productions are mainly located in the mangrove
belt.

How many hectares of shrimp farm are located in the mangrove
belt?

1. Convert the Mangrove.shp Theme to a
grid, with grid size 100 meters (Check your working directory and projection,
zoom in on the Theme, and make the conversion Output Grid Extent: Same As
Display).

2. Query the Mangrove grid with the Shrimp farm grid (Figure
14.18), which will give you Figure 14.19.

FIGURE 14.18Querying the mangrove grid with the
shrimp farm grid

FIGURE 14.19Shrimp farms in the mangrove forest of Pais Pesca

From the Theme table of the Map Query Theme you see that
approximately 15 589 pixels are selected with this query, meaning that 15 589 ha
of shrimp ponds are constructed in the mangrove belt of Pais Pesca (as one cell
is 100 metres by 100 metres). Query further the Mangrove grid with the surface
grid of the yields, after which you will get the following distribution (Table
14.3):

TABLE 14.3Area of shrimp ponds according to average
yields in the mangrove belt of Pais Pesca

Shrimp yield(kg/ha/crop)

Area(ha)

100-150

1 236

150-200

7 098

200-250

444

250-300

2 245

300-350

3 312

350-400

1 214

400-450

37

From this distribution we see that 8334 ha has low annual
yields of 100-200 kg/ha/crop, this apart from the favourable salinity of this
area. There must be a negative factor affecting the production (again, if your
numbers are not the same as given in the table, check your projection, to see
whether you use Equal-Area Cylindrical).

The major reason for the low productions, high disease rate,
and bad water quality might be that ponds are constructed on acid sulphate
soils. These soils are a normal phenomena in mangrove forests. No problems will
arise if they are left undisturbed. However, disturbing them (by for instance
digging ponds into these layers, or ploughing) results in leaching of sulphuric
acid and a reduced pH of the surface waters(de Graaf et al.,
1998).

Add the Acid_soil.shp Theme and this
relation becomes clear (Figure 14.20). How many hectares of shrimp farms are
constructed on acid sulphate soils? What would be your recommendation regarding
the shrimp ponds in the low salinity areas and in the mangrove belt? Try to
answer these questions by querying (do not forget to convert the
Acid_soil.shp Theme first).

14.3.2. Protection of fish stocks and
the creation of protected areas in Lake Kadim, Pais Pesca

Lake Kadim is the largest fresh water lake of Pais Pesca (220
km2). Fisheries at the lake is highly productive with an annual catch
of about 2 000 metric tonnes/year and it provides an income to 1 300
professional fishers. There are two major fish species:

The fishers demanded the creation of protected areas, where
fishing is closed during the spawning season for protection of the stocks.
However, they could not agree where to create the sanctuaries and asked the
Department of Fisheries (DoF) for assistance. In 1999, an extensive survey has
been carried out during the spawning season by DoF, to provide scientific data
for selection of spawning sanctuaries. Systematic sampling was carried out all
over the lake and the following data was collected:

Water depth
(m);

Water temperature
(°C);

Secchi depth (m);

Chlorophyll A(m
g/l);

Larval abundance clupeids
(number/m2);

Larval abundance of
carp(number/m2);

Abundance of adult carp
(number/m2).

Analysis of the data in GIS will support the decision where to
create fish spawning sanctuaries as it lets you understand the basic mechanisms
behind spawning and spatial distribution of the adult fish and their larvae and
eggs.

1. Open ArcView, New Project, New View.

2. Add the Themes Lake kadim data.shp(containing
all data and georeferences concerning the different sampling sites in Lake
Kadim), Lake kadim boundary.shp, and pais pesca
country.shp from the 14_Lake_Kadim folder.

3. Check the working directory and set the
projection.

4. As you are going to work at Lake Kadim only, you will first
need to make a grid of the Lake Kadim boundary shapefile, output
grid size 100 metres, so each pixel equals 1 hectare. Save this grid as
kadimbnd.

5. Set this grid as mask.

6. Activate lake Kadim data and make a surface
grid[23] (by interpolating) of the water
depth with IDW and a 100 metres grid cell size. If you do this
interpolation on the View it will take a long time, so Zoom in on Lake Kadim,
and make the Output Grid Extent: Same As Display. Save the new grid as
Kaddepth.

The different grids are presented below, (Figure 14.21 until
Figure 14.27). The first impressions are:

The deeper parts
are in the middle of the lake.

Low Secchi disc, high
chlorophyll concentration, high carp larvae density, high adult carp density and
higher surface water temperatures are in the northwest of the lake.

While the highest density of
clupeid larvae is in the centre of the lake.

FIGURE 14.21Water depth of Lake Kadim

FIGURE 14.22Secchi depth of Lake Kadim

FIGURE 14.23Chlorophyll concentration in Lake
Kadim

FIGURE 14.24Carp larvae density in Lake
Kadim

FIGURE 14.25Clupeid larvae density in Lake
Kadim

FIGURE 14.26Adult carp density in Lake
Kadim

FIGURE 14.27Water temperatures in Lake
Kadim

However, these are only impressions. Check if an analysis in
GIS can confirm the following hypothesis: Adult carp are mainly found in the
shallow waters. For this you need to query the water depth grid with the
adult carp grid (Figure 14.28). The results (Figure 14.29) show that there are
adult carp in shallow water, but only in the northwestern part of the lake. In
the shallow waters in the southeast, the densities of adult carp are
low.

FIGURE 14.28The query between water depth and adult
carps

FIGURE 14.29The results of the query between water
depth and adult carps

FIGURE 14.31The results of the query between adult
carps and water temperature

The carp is phytophageous so it could be that the abundance of
phytoplankton is a factor in the distribution of the adult carp. Therefore,
query the chlorophyll grid with the adult carp grid (Figure 14.32). The results
are presented in Figure 14.33. Indeed you see a very good match between the
chlorophyll and the adult carp distribution which could mean that chlorophyll or
the abundance of phytoplankton is a major factor for the distribution of adult
carp.

FIGURE 14.32The query between adult carps and
chlorophyll

FIGURE 14.33The results of the query between adult
carps and chlorophyll

What about the distribution of carp larvae, would that be
related to water temperature? Query the carp larvae grid with the temperature
grid (Figure 14.34). The results are presented in Figure 14.35.

FIGURE 14.34The query between carp larvae and
temperature

FIGURE 14.35The results of the query between carp
larvae and temperature

What about the water depth and the distribution of carp
larvae? (Figure 14.36). The result of the query between the water depth and the
abundance of carp larvae is presented in Figure 14.37. Again, you see shallow
parts where there are no carp larvae, especially the northeastern and southern
shorelines.

FIGURE 14.36The query between carp larvae and water
depth

FIGURE 14.37The results of the query between carp
larvae and water depth

Query the carp larvae grid with the chlorophyll grid (Figure
14.38). The results are presented in Figure 14.39. What are your
conclusions?

FIGURE 14.39The result of the query between carp
larvae and chlorophyll concentration

Query the carp larvae grid with the secchi depth grid (Figure
14.40). The results are presented in Figure 14.41. What are your
conclusions?

FIGURE 14.40The query between carp larvae and Secchi
disk depth

FIGURE 14.41The result of the query between carp
larvae and Secchi disk depth

A first look at the distribution pattern of the Clupeid larvae
indicates immediately that this is not related to chlorophyll or secchi depth.
However, it could be related to water depth or surface water temperature as the
highest densities are found in the middle of Lake Kadim.

Query the clupeid larvae grid with the water
depth grid (Figure 14.42). The results are presented in Figure 14.43. The
created grid is reasonable but there are still high densities of clupeid larvae
outside the created grid. Make the grid again but now with a water depth of 6
metres or more. Does this improve the situation?

FIGURE 14.42The query between clupeid larvae and
water depth

FIGURE 14.43The results of the query between clupeid
larvae and water depth

Query the Clupeid larvae grid with the water temperature grid
(Figure 14.44). The results are presented in Figure 14.45. The created grid
matches better then the water depth especially at spot points and it seems that
water temperature, especially in the central part is an important factor for the
distribution of clupeid larvae.

FIGURE 14.44The query between clupeid larvae and
water temperature

FIGURE 14.45The result of the query between clupeid
larvae and water temperature

Conclusions

Adult Carpio
felixiensis mainly concentrates in the shallow waters located in the
northwestern part of Lake Kadim. The major driving force behind this mechanism
is most likely feeding as its distribution follows the distribution pattern of
chlorophyll. The newly born larvae of Carpio felixiensis follow the same
distribution pattern.

The larvae of Clupea
pepeiensis have a completely different distribution as they aggregate in the
deeper waters. In the deeper waters this distribution is most likely related to
water temperature.

It should however be realized that the relations made visible
through querying of grids is a kind of visualisation only. Direct relations
between adult carp densities and chlorophyll or clupeid larvae and water
temperature are not quantified or statistically verified. This can only be done
through regression analysis and this will be discussed in the next
chapter.

For the establishment of a fish sanctuary these findings have
complications as closing fisheries in the northeastern shallow and eutrophic
waters will certainly protect the carp stocks but this will not protect the
clupeid stocks. Only a complete closure of fisheries for several months will
protect both stocks.

[16] Topolevel of
course.[17] The floodlevels of
October, in the column Oct.[18] Because they are outside
the mask you have set before the analysis![19] If you forget this the
file(s) will have the name Calc1, Calc2, Calc3, etc.[20] The species used is
mainly Penaeus monodon.[21] Remember the naming
convention![22] This should be around 5
855 hectares.[23] Also create this grid
with Kriging see Annex C: Kriging on page 155.